Makeflow = Make + Workflow
Makeflow is a workflow engine for executing large
complex workflows on clusters, clouds, and grids.
Makeflow is very similar to traditional Make, so if you
can write a Makefile, then you can write a Makeflow.
A workflow can be just a few commands chained together,
or it can be a complex application consisting of thousands
of tasks. It can have an arbitrary DAG structure and
is not limited to specific patterns.
Makeflow is used to represent complex applications
in fields such as data mining, high energy physics,
image processing, and bioinformatics.
Makeflow is portable. A workflow is written in a technology
neutral way, and then can be deployed to a variety of different
systems without modification, including local execution on
a single multicore machine as well as batch systems like HTCondor, SGE,
Torque or the bundled Work Queue
system. The same specification works for all systems, so you can easily grow your application from one machine up to thousands.
Makeflow differs from other distributed and parallel make tools
in that it does not require a distributed filesystem.
You can use it to harness whatever machines you have available,
and Makeflow handles the data transfer and caching. In addition,
Makeflow is highly fault tolerant: it can crash or be killed,
and upon resuming, will reconnect to running jobs and continue
where it left off by making use of a transaction log.
For More Information
Makeflow User's Manual
Makeflow Tutorial Slides
Getting Help with Makeflow
(Showing papers with tag makeflow. See all papers instead.)
Nicholas Hazekamp, Olivia Choudhury, Sandra Gesing, Scott Emrich, and Douglas Thain,
Poster: Expanding Tasks of Logical Workflows into Independent Workflows for Improved Scalability,
IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pages 548-549, January, 2014. ISBN: 10.1109/CCGrid.2014.84
Peter Bui, Li Yu, Andrew Thrasher, Rory Carmichael, Irena Lanc, Patrick Donnelly, Douglas Thain,
Scripting distributed scientific workflows using Weaver,
Concurrency and Computation: Practice and Experience, November, 2011. DOI: 10.1002/cpe.1871
Andrew Thrasher, Rory Carmichael, Peter Bui, Li Yu, Douglas Thain, and Scott Emrich,
Taming Complex Bioinformatics Workflows with Weaver, Makeflow, and Starch,
Workshop on Workflows in Support of Large Scale Science, pages 1-6, November, 2010. DOI: 10.1109/WORKS.2010.5671858
Li Yu, Christopher Moretti, Andrew Thrasher, Scott Emrich, Kenneth Judd, and Douglas Thain,
Harnessing Parallelism in Multicore Clusters with the All-Pairs, Wavefront, and Makeflow Abstractions,
Journal of Cluster Computing, 13(3), pages 243-256, September, 2010. DOI: 10.1007/s10586-010-0134-7